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Citation: Afeef, S.; Tolfrey, K.;
Zakrzewski-Fruer, J.K.; Barrett, L.A.
Performance of the FreeStyle Libre
Flash Glucose Monitoring System
during an Oral Glucose Tolerance
Test and Exercise in Healthy
Adolescents. Sensors 2023,23, 4249.
https://doi.org/10.3390/s23094249
Academic Editor: Helmut
Karl Lackner
Received: 20 March 2023
Revised: 19 April 2023
Accepted: 21 April 2023
Published: 25 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Article
Performance of the FreeStyle Libre Flash Glucose Monitoring
System during an Oral Glucose Tolerance Test and Exercise in
Healthy Adolescents
Sahar Afeef 1,2, Keith Tolfrey 1, Julia K. Zakrzewski-Fruer 3and Laura A. Barrett 1,*
1School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
2Clinical Nutrition Department, Faculty of Applied Medical Sciences, King Abdulaziz University,
Jeddah 21589, Saudi Arabia
3Institute for Sport and Physical Activity Research, University of Bedfordshire, Bedford MK41 9EA, UK
*Correspondence: l.a.barrett@lboro.ac.uk
Abstract:
This study’s aim was to assess FreeStyle Libre Flash glucose monitoring (FGM) performance
during an oral glucose tolerance test (OGTT) and treadmill exercise in healthy adolescents. This
should advance the feasibility and utility of user-friendly technologies for metabolic assessments in
adolescents. Seventeen healthy adolescents (nine girls aged 12.8
±
0.9 years) performed an OGTT
and submaximal and maximal treadmill exercise tests in a laboratory setting. The scanned interstitial
fluid glucose concentration ([ISFG]) obtained by FGM was compared against finger-prick capillary
plasma glucose concentration ([CPG]) at 0 (pre-OGTT),
−
15,
−
30,
−
60,
−
120 min post-OGTT,
pre-,
mid-, post- submaximal exercise, and pre- and post- maximal exercise. Overall mean absolute
relative difference (MARD) was 13.1
±
8.5%, and 68% (n= 113) of the paired glucose data met the
ISO 15197:2013
criteria. For clinical accuracy, 84% and 16% of FGM readings were within zones
A and B in the Consensus Error Grid (CEG), respectively, which met the ISO 15197:2013 criteria
of having at least 99% of results within these zones. Scanned [ISFG] were statistically lower than
[CPG] at 15 (
−1.16 mmol·L−1
,
p< 0.001
) and 30 min (
−
0.74 mmol
·
L
−1
,p= 0.041) post-OGTT. Yet,
post-OGTT glycaemic responses assessed by total and incremental areas under the curve (AUCs)
were not significantly different, with trivial to small effect sizes (p
≥
0.084, d = 0.14–0.45). Further,
[ISFGs] were not different from [CPGs] during submaximal and maximal exercise tests (interaction
p
≥
0.614). FGM can be a feasible alternative to reflect postprandial glycaemia (AUCs) in healthy
adolescents who may not endure repeated finger pricks.
Keywords:
accuracy; flash glucose monitor; continuous glucose monitor; interstitial glucose; capillary
glucose; postprandial glucose; OGTT; exercise; MARD; healthy adolescents
1. Introduction
Postprandial glycaemia has been implicated in developing cardiometabolic diseases [
1
].
Even in healthy individuals with normoglycaemia, those with a greater glycaemic response
after feeding have reduced insulin sensitivity and impaired beta-cell function and, there-
fore, are at higher risk of developing type 2 diabetes (T2D) and cardiovascular disease
(CVD) [
2
–
4
]. Thus, moderating postprandial glycaemia through dietary manipulation
and physical activity is essential for disease prevention. With the increasing prevalence
of prediabetes in the UK population (aged 16 to 39 years) from 2.8% in 2003 to 15.6% in
2011 [
5
] and 2.4 million people (aged 17 or greater) in England recorded by GP practices
as having non-diabetic hyperglycaemia for the period January 2020 to March 2021 [
6
],
devices such as continuous glucose monitoring (CGM) systems have become of greater
importance for research purposes. Researchers have utilised CGM in healthy adolescents
without diabetes to understand the influence of lifestyle behaviours such as dietary intake
and physical activity on glycaemic responses [
7
,
8
]. Yet, the validity of CGM has not been
Sensors 2023,23, 4249. https://doi.org/10.3390/s23094249 https://www.mdpi.com/journal/sensors
Sensors 2023,23, 4249 2 of 14
well-established in this sub-group of the population [
9
]. One known limitation of the
use of CGM devices is a time delay due to the diffusion processes of glucose from the
vascular to the interstitial space, which is a particular problem when glucose concentrations
are changing rapidly (e.g., during high intensity exercise or when foods containing high
amounts of glucose are ingested). Although the time lag of glucose from the intravascular
to the interstitial compartments has been investigated in adults [
10
], this has yet to be
investigated in healthy adolescents and could be different, particularly during exercise
when substrate utilisation is known to be different in adults due to metabolic and hormonal
differences [
11
]. Thus, it is important to validate CGM, as it would inform preventative
approaches for cardiometabolic disease and ultimately help to improve the health outcomes
of this vulnerable population.
The Flash glucose monitor (FGM; FreeStyle Libre, Abbott Diabetes Care Inc.), a type
of CGM system, has the advantage of being less invasive than the commonly used method
of finger-prick blood sampling in paediatric research. Indeed, FGM has been shown to be
less painful and highly acceptable over a finger-brick technique using a glucometer among
adolescents with type 1 diabetes (T1D) [
12
,
13
]. However, it must be acknowledged that the
cost of the devices can be prohibitive for some users, and issues with the insertion, adhesion
and removal of sensors have been reported [
14
]. The FGM is inserted once into the back
of the upper arm to measure interstitial fluid glucose concentration ([ISFG]) via
a glucose
oxidase reaction, and it uses algorithms to estimate blood glucose concentration [
15
].
Furthermore, the FGM does not require user calibration (i.e., it is factory-calibrated) using
a glucometer, which is advantageous for healthy adolescents who are unfamiliar with this
technique. The FGM provides two types of data: scanned glucose (manual) and historic
glucose (automatic). The patients/participants can view the scanned glucose concentrations
on the reader following manual sensor scanning. The historic glucose concentrations are
recorded every 15 min and require active (manual) scanning of the sensor every 8 h to store
the data and avoid its loss. Thus, the FGM can provide broader information on glycaemic
variability under free-living and laboratory conditions. Such information would not be
detected using the traditional finger-prick method, which is not appropriate for use over
extended durations with such high sampling frequencies. This is important from a health
perspective because glycaemic variability is recognised as a crucial component of glycaemic
control [
16
] associated with increased CVD risk [
4
,
17
]. For this reason, comparing FGM
with the traditional finger-prick method is needed in healthy adolescents without diabetes
to facilitate its application in research.
The FGM has been shown to be safe and have acceptable accuracy (based on overall
mean absolute relative difference (MARD); 11.7–13.9%) in young people with diabetes; yet,
its performance deteriorated at low glucose concentrations and when the rate of glucose
change was high [
12
,
18
,
19
]. There is a lack of information on the ability of FGM to detect
glycaemic responses to feeding and exercise in healthy adolescents. To our knowledge,
only a few studies have examined the validity of FreeStyle Libre in response to feeding
and exercise in adults without diabetes [
20
–
23
] with only a single study validating the
FreeStyle Libre Pro version in healthy children aged 7 to 12 years [
9
]. One study showed
that FGM overestimated venous plasma glucose concentrations by 0.63 to 1.50 mmol
·
L
−1
30 to 90 min after glucose loading in a small sample (n= 7) of healthy adults [
23
]. However,
two recent studies demonstrated that while FGM tended to underestimate plasma glucose
concentrations by 1.03 to 1.61 mmol
·
L
−1
in response to a standard breakfast meal, the
overall accuracy of FGM was deemed clinically acceptable in healthy adults [
20
,
21
]. It is
difficult to identify the reason for such contrasting results, but it could be partly due to
the differences in test meals and/or participant characteristics. Physical activity can also
impact glucose concentrations, yet the accuracy of FGM during different exercise intensities
does not appear to have been determined in healthy adolescents. Two recent studies with
healthy adults showed conflicting results of FGM performance during exercise. One study
with healthy adults showed a reduced accuracy of FGM during high intensity intermittent
exercise, as indicated by high values in the clinically unsafe zone (i.e., 10.5% in zone D) after
Sensors 2023,23, 4249 3 of 14
consuming a carbohydrate-rich meal [
22
]. Another study showed FGM underestimated
plasma glucose concentrations during different walking conditions, with 99.6 to 100% of
glucose values within the clinically acceptable zones A and B [
21
]. Yet, findings based
on adults may not apply to adolescents’ distinct hormonal and metabolic profiles [
11
].
Therefore, the aim of this study was to compare blood glucose concentrations obtained by
FGM (i.e., [ISFG]) against [CPG] (reference method) in response to an oral glucose tolerance
test (OGTT) and treadmill exercise at different intensities in a laboratory setting in healthy
adolescents to assess the degree of measurement bias.
2. Materials and Methods
This was a prospective, single-arm study performed in healthy adolescents. The
study was conducted between November 2019 and March 2020 in accordance with the
ethical standards of Loughborough University Ethics Committee (HPSC reference number:
R19-P147). The ethical approval was obtained in October 2019. Participants were recruited
through school assemblies, where the aims and procedures of the study were presented.
Written assent was obtained from each participant, and written informed consent was
obtained from a parent/guardian.
2.1. Eligibility Criteria
The participants were included providing they were aged 11–14 years, not on med-
ication or living with a disease (e.g., diabetes) that may affect glucose metabolism, not
presenting with any injuries or conditions that prevented them from performing any exer-
cise task (e.g., musculoskeletal problems, epilepsy, uncontrolled exercise-induced asthma),
and had no skin conditions (e.g., allergy) that may affect glucose sensor employment.
2.2. Study Design
An FGM (FreeStyle Libre, Abbott Diabetes Care Inc., UK) was inserted in the par-
ticipant’s non-dominant upper arm according to the manufacturer’s instructions after
the skin was cleaned with an alcohol swab, and it was worn for 14 days. An adhesive
patch (Hypafix
®
transparent, BSN medical) was placed over the glucose monitor to ensure
it was attached securely, and the participants were given extra patches to replace those
that became dirty or lost adhesion. To avoid the reported inaccuracy of the monitor on
the insertion day [
24
], the monitor was validated against finger-prick [CPG] on day 2 or
3 of
sensor wear in response to feeding and exercise. Participants were asked to refrain
from engaging in any physical activity of moderate–vigorous intensity 24 h preceding the
laboratory visit. Additionally, participants were asked to consume a standardised cereal
bar at 19:20, after which they were asked to drink only water to ensure they had fasted for
12 h when they arrived at the lab the following day.
Participants arrived at the laboratory wearing an FGM, and each time a capillary blood
sample was taken, the FGM was scanned manually with the reader device by a researcher
to obtain simultaneous glucose readings from the two sites. Anthropometry was conducted
first, and then participants rested for 10 min before providing a fasting capillary blood
sample. Then, they were asked to consume a drink containing 1.75 g glucose per kg body
mass (with a maximum of 75 g of glucose in line with standard OGTT procedure) within
5 min [25]
. Subsequent capillary blood samples were collected at 15-, 30-, 60-, and 120-min
intervals after the glucose drink consumption was initiated. After completing the OGTT,
participants were given a breakfast meal to satisfy their hunger (white bread, butter, jam,
and fruit juice, which provided 422 kcal (1766 kJ); 73% carbohydrate; 16% protein; 8% fat).
One hour after breakfast consumption, the participants completed a 4
×
4 min
stage submaximal treadmill exercise test. The treadmill speed was increased after each
four-minute
stage: 4, 6, 8 and 10 km
·
h
−1
for boys and 4, 5, 6.5, 8 km
·
h
−1
for girls. The
first
two stages
equated to walking speeds, and the second two stages equated to jog-
ging/running speeds. Capillary blood samples were taken pre-, midway (at minute 8)
and immediately post- the submaximal exercise test. Participants then rested for 1 h
Sensors 2023,23, 4249 4 of 14
before completing a maximal exercise test. In the maximal test, the participants ran at
a pre-determined individual fixed speed (inter-participant range 7 to 10 km
·
h
−1
) while
the treadmill belt gradient was raised by 1% every minute (1 min stages) until volitional
termination was attained. Finger-prick capillary blood samples were taken immediately
pre- and post- the maximal exercise test. Expired air samples were collected during at
least the last three one-minute stages of the maximal exercise test using standard Douglas
bag methods to measure peak oxygen consumption (
.
VO2
peak). The study protocol is
presented in Figure 1.
Sensors 2023, 23, x FOR PEER REVIEW 4 of 15
One hour after breakfast consumption, the participants completed a 4 × 4 min stage
submaximal treadmill exercise test. The treadmill speed was increased after each four-
minute stage: 4, 6, 8 and 10 km∙h−1 for boys and 4, 5, 6.5, 8 km∙h−1 for girls. The rst two
stages equated to walking speeds, and the second two stages equated to jogging/running
speeds. Capillary blood samples were taken pre-, midway (at minute 8) and immediately
post- the submaximal exercise test. Participants then rested for 1 h before completing a
maximal exercise test. In the maximal test, the participants ran at a pre-determined indi-
vidual xed speed (inter-participant range 7 to 10 km∙h−1) while the treadmill belt gradient
was raised by 1% every minute (1 min stages) until volitional termination was aained.
Finger-prick capillary blood samples were taken immediately pre- and post- the maximal
exercise test. Expired air samples were collected during at least the last three one-minute
stages of the maximal exercise test using standard Douglas bag methods to measure peak
oxygen consumption (V
O2 peak). The study protocol is presented in Figure 1.
Figure 1. Schematic of the study protocol.
2.3. Anthropometry and Physical Maturity
Stature was measured using a stadiometer (The Leicester height measure, Seca Ltd.,
Birmingham, UK) to the nearest 0.01 m. Body mass (BM) was measured, and percentage
body fat was estimated using bioelectrical impedance (Tanita BC-418MA, Tanita Corpora-
tion, Tokyo, Japan) to the nearest 0.1 kg and 0.1%, respectively, while participants stood
barefoot wearing light clothes. Body mass index (BMI) was calculated by dividing the
body mass (kg) by the stature squared (m2). Consequently, weight status was determined
using age and sex-specic BMI cut o points [26]. Waist circumference was taken from the
central point between the 10th rib and the iliac crest using a non-exible tape measure [27].
Physical maturity was estimated through a self-assessment of secondary sexual character-
istics. The scale ranges from 1 (prepubescent) to 5 (adult) [28].
Figure 1. Schematic of the study protocol.
2.3. Anthropometry and Physical Maturity
Stature was measured using a stadiometer (The Leicester height measure, Seca Ltd.,
Birmingham, UK) to the nearest 0.01 m. Body mass (BM) was measured, and percentage
body fat was estimated using bioelectrical impedance (Tanita BC-418MA, Tanita Corpora-
tion, Tokyo, Japan) to the nearest 0.1 kg and 0.1%, respectively, while participants stood
barefoot wearing light clothes. Body mass index (BMI) was calculated by dividing the body
mass (kg) by the stature squared (m
2
). Consequently, weight status was determined using
age and sex-specific BMI cut off points [
26
]. Waist circumference was taken from the central
point between the 10th rib and the iliac crest using a non-flexible tape measure [
27
]. Physi-
cal maturity was estimated through a self-assessment of secondary sexual characteristics.
The scale ranges from 1 (prepubescent) to 5 (adult) [28].
2.4. Blood Sampling and Analyses
To enhance blood flow to the hand, the participants were asked to immerse their
whole hand into a hot water (40
◦
C) container for 5 min, after which the hand was dried
immediately, and a finger was cleaned with an alcohol swab and then pricked with a lancet
(Unistick 3 Extra, Owen Mumford, UK). The first drop of blood was wiped, and 300 to
600 µL
of blood was drawn into microvette tubes (Sarstedt Ltd., Leicester, UK). The sample
tubes were placed immediately into a centrifuge at 12,800
×
gfor 15 min (Eppendorf 5415c,
Sensors 2023,23, 4249 5 of 14
Hamburg, Germany) to allow collection and storage of the resulting plasma at
−
80
◦
C for
subsequent batch analysis. Plasma glucose concentration was analysed using a benchtop
analyser (Pentra 400; HORIBA ABX Diagnostics, Montpellier, France) using enzymatic,
colourimetric methods (HORIBA ABX Diagnostics). Further analyses of fasting plasma
samples were completed to determine insulin concentrations using an enzyme-linked
immunosorbent assay (Mercodia AB, Uppsala, Sweden). The intra-assay coefficients of
variation for the duplicate samples were 0.7% for plasma glucose and 4.4% for plasma
insulin. Fasting plasma glucose and insulin concentrations were used to calculate the
homeostatic model assessment of insulin resistance (HOMA-IR) [29].
2.5. FGM Accuracy Assessments and Statistical Analyses
The scanned [ISFG] were paired with [CPG] measurements at 10 time points. The glu-
cose data were divided into three segments representing three distinct events (i.e., during
OGTT (5 time points), submaximal exercise test (3 time points) and maximal exercise test
(2 time points)
) to evaluate [ISFG] and [CPG] agreement to feeding and exercise indepen-
dently. Matched glucose values during OGTT (i.e., at 5 time points, including fasting)
were used to calculate total (tAUC) and incremental (iAUC) areas under the curve, peak
glucose and time to peak using a time series response analyser [
30
]. Several methods
were performed to assess the sensor accuracy of FGM, as there are no universally agreed
standards to assess CGM accuracy [31].
The mean absolute (non-directional) relative difference (MARD) of the pooled glucose
measurements at segmented events and at individual time-points were calculated using
the following formula: (|ISFG
−
CPG|)/CPG
×
100. Additionally, individual MARD
was calculated using all paired glucose for each individual to examine the inter-individual
variation. Correlation is described using Pearson’s correlation coefficient. The magnitude
of effect sizes for the correlation coefficients of 0.10, 0.30 and 0.50 are small, medium, and
large, respectively [
32
]. The percentage of paired results fulfilling the International Organi-
zation for Standardization (ISO) 15197:2013 criteria (mainly developed for glucometers)
was assessed. The ISO 15197:2013 requires (1) 95% of results from the device to fall within
±0.83 mmol·L−1
(15 mg
·
dL
−1
) when reference glucose <5.56 mmol
·
L
−1
(100 mg
·
dL
−1
) or
within
±
15% when reference glucose values
≥
5.56 mmol
·
L
−1
; (2) at least 99% of results
have to be within zones A and B in the Consensus Error Grid (CEG) (also known as the
Parkes Error Grid) [
33
]. Bland–Altman 95% (ratio) limits of agreement (LoA) were used to
assess the agreement between [ISFG] and [CPG] for the segmented events. The maximal ex-
ercise glucose samples were transformed using a natural logarithm (log
e
)
due to
significant
or meaningful heteroscedastic random errors; thus, 95% ratio LoA were calculated.
Linear mixed models repeated for site and time points were used to examine differ-
ences between [ISFG] and [CPG] during the three distinct events. The differences between
tAUC, iAUC, peak glucose and time to peak during OGTT were examined using linear
mixed models repeated for the site ([ISFG] and [CPG]). OGTT glucose, iAUC, peak glucose
and time to peak data were log
e
transformed due to non-normal residuals. These data
are presented as geometric mean (95% confidence interval [CI]), and analyses are based
on ratios of geometric means and 95% CI for ratios. The effect size (d) was calculated
to describe the magnitude of difference between glucose measurements according to the
following thresholds: trivial (<0.2), small (
≥
0.2), moderate (
≥
0.5) and large (
≥
0.8) [
32
].
Statistical analyses were completed using SPSS (version 25.0; SPSS Inc., Chicago, IL). Val-
ues are expressed as mean
±
SD unless stated otherwise, and statistical significance was
accepted at p< 0.05.
3. Results
Nineteen healthy participants were recruited, and all completed the study.
Two participants
were excluded because they did not fast. Of the remaining seventeen participants, one
did not complete the submaximal and maximal exercise test due to illness. Thus, the
accuracy analyses included 17 participants during OGTT (i.e., 85 glucose pairs) and
Sensors 2023,23, 4249 6 of 14
16 participants
during submaximal (i.e., 48 glucose pairs) and maximal exercise tests
(i.e., 32 glucose pairs). The participants’ characteristics are presented in Table 1. The
mean age was 12.8
±
0.9 years, and nine were girls. One participant was overweight,
and two participants were thin (grade 1) according to age and sex-specific BMI cut off
points [
26
]. Four participants were classified as ‘at risk’ according to age-, sex-, and BMI-
specific percentiles of HOMA-IR [34].
Table 1. Participant characteristics (n= 17).
Variable Mean ±SD Range
Age (y) 12.8 ±0.9 11.5–14.4
Stature (m) 1.56 ±0.1 1.35–1.75
Body mass (kg) 44.7 ±7.2 30.3–56.9
BMI (kg·m−2)18.4 ±2.1 15.4–22.1
Body fat (%) 22.2 ±4.9 12.7–31.5
Waist circumference (cm) 60.3 ±8.9 46.0–72.0
Breast development * 4 (1) 1–4
Genital development * 2 (1) 1–3
Pubic hair development * 3 (2) 1–5
HOMA-IR 1.62 ±1.01 0.44–3.74
.
VO2
peak (mL
·
kg
−1·
min
−1
) **
42.3 ±10.1 31.6–58.8
BMI, body mass index;
.
VO2
, oxygen consumption; HOMA-IR, homeostatic model assessment of insulin resistance.
* Self-assessment—median (interquartile range). Two participants did not complete the assessment (n= 15).
** One participant did not complete both exercise tests, and maximal exercise test data were not available for
another participant due to a technical error (n= 15).
FGM Accuracy
Overall, 68% (n= 113) of the paired glucose data met the ISO 15197:2013 criteria and
were within the acceptable range, while the rest (32%, n= 52) were outside the set bound-
aries (Figure 2). The MARD of the 68% of glucose data meeting the ISO 15197:2013 criteria
was 8.6
±
4.9% and 23.0
±
5.8% for the 32% that did not meet the ISO 15197:2013 criteria.
The overall MARD was 13.1
±
8.5%. The individual MARDs showed large inter-individual
variations that ranged between 9.4% and 18.2%. Individual MARDs did not correlate with
any participant characteristics, including age, BMI, %body fat, waist circumference, matu-
rity status and
.
VO2
peak (p
≥
0.150), nor did they differ between boys and girls
(p= 0.380)
;
it was not possible to identify an explicit cause for the inter-individual variability.
When examining the clinical accuracy of FGM on the CEG (Figure 3), 84% (n= 139) and
16% (n= 26) of the FGM readings were found in zone A (which were classified as clinically
accurate measurements) and zone B (altered clinical action, little or no effect on the clinical
outcome), respectively, which met the ISO 15197:2013 criteria of having at least 99% of
results within zones A and B. These results indicate that the deviated readings of FGM
from the reference method (i.e., sensor error) would not severely impact
clinical decisions.
The differences between the glucose measurements of the two methods are presented
in Table 2. Overall, FGM provided lower [ISFG] on average than [CPG] during OGTT,
with a small effect size (p< 0.001, d = 0.35). While post-OGTT [ISFG] responses resulted in
0.28 mmol
·
L
−1
(~4%) lower tAUC and 0.10 mmol
·
L
−1
(~5%) lower iAUC on average than
[CPG], the differences were not statistically significant, with small and trivial effect sizes,
respectively. Pairwise comparisons between [ISFG] and [CPG] at each time point revealed
significantly large and moderate differences at 15 and 30 min, respectively (Table 2and
Figure 4A). Peak [ISFG] was lower by 0.54 mmol
·
L
−1
(~7%) compared to [CPG] during
OGTT, and the mean time to peak for [ISFG] was 37 min (95% CI 31 to 43 min) and 30 min
(95% CI 25 to 35 min) for [CPG], indicating a lag time of 7 min on average. Non-significant
site by timepoint interactions were observed during the exercise tests (p
≥
0.614), indicating
that the pattern of [ISFG] was similar to [CPG] across the time points (Figure 4B,C).
Sensors 2023,23, 4249 7 of 14
Sensors 2023, 23, x FOR PEER REVIEW 7 of 15
Figure 2. Scaer plot showing the dierences in measurements between interstitial uid glucose
concentration ([ISFG]) obtained by a Flash glucose monitor and capillary plasma glucose concentra-
tion ([CPG]) during oral glucose tolerance test (red dots), submaximal (green dots) and maximal
exercise (blue dots) tests. Dashed lines depict the accuracy boundaries applied based on ISO
15197:2013.
Figure 3. Consensus error grid (CEG) analysis of Flash glucose monitor (FGM). A total of 165 glu-
cose pairs of [ISFG] obtained by an FGM and capillary plasma glucose concentration (reference
method) were ploed in the CEG. According to ISO 15197:2013, 99% of measurement results shall
be within CEG zones A and B. The FGM shows 100% of results within CEG zones A and B. Dashed
lines are the boundaries of the dierent zones, implying dierent degrees of risk posed by inaccu-
rate measurement. Zone A—no eect on clinical action; zone B—altered clinical action with lile or
no eect on clinical outcome; zone C—altered clinical action and likely to aect clinical outcome;
zone D—altered clinical action which could have signicant medical risk; zone E—altered clinical
action, could have dangerous consequences.
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
0.00 2.00 4.00 6.00 8.00 10.00 12.00
Difference in Glucose Measurement
[ISFG] −[CPG] (mmol∙L−1)
Capillary plasma glucose concentration (mmol∙L−1)
Figure 2.
Scatter plot showing the differences in measurements between interstitial fluid glucose con-
centration ([ISFG]) obtained by a Flash glucose monitor and capillary plasma glucose concentration
([CPG]) during oral glucose tolerance test (red dots), submaximal (green dots) and maximal exercise
(blue dots) tests. Dashed lines depict the accuracy boundaries applied based on ISO 15197:2013.
Sensors 2023, 23, x FOR PEER REVIEW 7 of 15
Figure 2. Scaer plot showing the dierences in measurements between interstitial uid glucose
concentration ([ISFG]) obtained by a Flash glucose monitor and capillary plasma glucose concentra-
tion ([CPG]) during oral glucose tolerance test (red dots), submaximal (green dots) and maximal
exercise (blue dots) tests. Dashed lines depict the accuracy boundaries applied based on ISO
15197:2013.
Figure 3. Consensus error grid (CEG) analysis of Flash glucose monitor (FGM). A total of 165 glu-
cose pairs of [ISFG] obtained by an FGM and capillary plasma glucose concentration (reference
method) were ploed in the CEG. According to ISO 15197:2013, 99% of measurement results shall
be within CEG zones A and B. The FGM shows 100% of results within CEG zones A and B. Dashed
lines are the boundaries of the dierent zones, implying dierent degrees of risk posed by inaccu-
rate measurement. Zone A—no eect on clinical action; zone B—altered clinical action with lile or
no eect on clinical outcome; zone C—altered clinical action and likely to aect clinical outcome;
zone D—altered clinical action which could have signicant medical risk; zone E—altered clinical
action, could have dangerous consequences.
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
0.00 2.00 4.00 6.00 8.00 10.00 12.00
Difference in Glucose Measurement
[ISFG] −[CPG] (mmol∙L−1)
Capillary plasma glucose concentration (mmol∙L−1)
Figure 3.
Consensus error grid (CEG) analysis of Flash glucose monitor (FGM). A total of 165 glucose
pairs of [ISFG] obtained by an FGM and capillary plasma glucose concentration (reference method)
were plotted in the CEG. According to ISO 15197:2013, 99% of measurement results shall be within
CEG zones A and B. The FGM shows 100% of results within CEG zones A and B. Dashed lines
are the boundaries of the different zones, implying different degrees of risk posed by inaccurate
measurement. Zone A—no effect on clinical action; zone B—altered clinical action with little or no
effect on clinical outcome; zone C—altered clinical action and likely to affect clinical outcome; zone
D—altered clinical action which could have significant medical risk; zone E—altered clinical action,
could have dangerous consequences.
Sensors 2023,23, 4249 8 of 14
Table 2.
Differences between [ISFG] obtained by FGM and [CPG] at each time point during OGTT
(n= 17), submaximal and maximal exercise tests (n= 16).
[ISFG]
(mmol·L−1) *
[CPG]
(mmol·L−1) *
Mean Difference
(95% CI) *
MARD
(%) p-Value Effect Size
Overall OGTT 5.96 (5.65 to 6.29) 6.43 (6.09 to 6.78) −7.3% (−11 to −3%) 13.4 ±5.0 <0.001 0.35
0 min 4.52 (4.18 to 4.89) 4.78 (4.42 to 5.17) −5.4% (−14 to 4%) 14.1 ±6.3 0.240 0.57
15 min 5.67 (5.24 to 6.13) 6.83 (6.32 to 7.39) −
17.1% (
−
24 to
−
9%)
17.5 ±11.2 <0.001 1.21
30 min 7.42 (6.86 to 8.02) 8.17 (7.56 to 8.84) −9.2% (−17 to 0%) 10.6 ±6.1 0.041 0.55
60 min 6.76 (6.25 to 7.31) 6.73 (6.22 to 7.28) 0.5% (−8 to 10%) 13.4 ±7.4 0.923 0.17
120 min 5.86 (5.42 to 6.34) 6.12 (5.66 to 6.62) −4.2% (−13 to 5%) 11.4 ±7.5 0.365 0.29
Glucose iAUC 1.79 (1.47 to 2.20) 1.89 (1.54 to 2.31) −4.9% (−19 to 12%) 22.1 ±15.1 0.529 0.14
Glucose tAUC 6.52 ±1.06 6.80 ±0.64 −0.28 (−0.61 to 0.04) 8.5 ±5.8 0.084 0.45
Peak glucose 7.81 (7.24 to 8.43) 8.35 (7.74 to 9.01) −6.5% (−12 to −1%) 11.0 ±5.6 0.032 0.52
Overall submaximal
exercise 5.11 ±1.06 5.36 ±0.93 −0.25 (−0.54 to 0.04) 14.0 ±4.4 0.093 0.27
Pre submaximal
exercise 5.77 ±1.04 6.14 ±0.76 −0.37 (−0.88 to 0.14) 11.4 ±7.7 0.149 0.49
Mid submaximal
exercise 4.83 ±0.94 4.88 ±0.73 −0.05 (−0.55 to 0.46) 14.6 ±9.4 0.859 0.07
Post submaximal
exercise 4.72 ±0.94 5.05 ±0.75 −0.34 (−0.84 to 0.17) 16.1 ±9.0 0.193 0.44
Overall maximal
exercise 4.96 ±0.85 5.06 ±0.78 −0.11 (−0.38 to 0.17) 11.0 ±5.0 0.441 0.14
Pre maximal exercise 5.49 ±0.84 5.54 ±0.63 −0.04 (−0.43 to 0.35) 13.2 ±8.5 0.830 0.08
Post maximal exercise 4.42 ±0.43 4.59 ±0.61 −0.17 (−0.56 to 0.22) 8.8 ±6.4 0.382 0.64
[ISFG], interstitial fluid glucose concentration; FGM, Flash glucose monitor; [CPG], capillary plasma glucose
concentration; OGTT, oral glucose tolerance test; iAUC, incremental area under curve; tAUC, total area under
curve; MARD, mean absolute relative difference. Mean differences between [ISFG] and [CPG] were examined
using linear mixed models. Post-hoc pairwise comparisons were examined with the Holm–Bonferroni correction
for multiple comparisons. A p-value < 0.05 depicts statistical significance. * For log transformed data, values are
presented as geometric means, and corresponding 95% CI and pairwise comparisons are presented as percentage
difference (%) based on ratios of geometric means and corresponding 95% CI (%). For untransformed (normally
distributed) data, values are expressed as mean
±
standard deviation (SD), and pairwise comparisons are
presented as mean absolute difference and corresponding 95% CI.
There were large, positive correlations between paired [ISFG] and [CPG] values during
the OGTT (r = 0.789, 95% CI 0.69 to 0.86, r
2
= 62%, p< 0.001) and the submaximal (
r = 0.646
,
95% CI 0.44 to 0.79, r
2
= 42%, p< 0.001) and maximal (r = 0.622, 95% CI 0.35 to 0.80,
r2= 39%
,
p< 0.001) exercise tests. Bland–Altman plots showed lower [ISFG] than [CPG] (i.e., negative
biases) during the OGTT (
−
0.47 mmol
·
L
−1
,p< 0.001; 95% LoA,
−
2.40 to
1.54 mmol·L−1)
and submaximal exercise (
−
0.25 mmol
·
L
−1
,p= 0.046; 95% LoA,
−
1.92 to 1.42 mmol
·
L
−1
),
but not during the maximal exercise test (0.98, p= 0.326; 95% ratio LoA, 0.75 to 1.27).
Sensors 2023,23, 4249 9 of 14
Sensors 2023, 23, x FOR PEER REVIEW 9 of 15
Figure 4. Changes in interstitial uid glucose ([ISFG], open circle) and capillary plasma glucose
([CPG], open square) concentrations during an oral glucose tolerance test (A), submaximal (B) and
maximal (C) exercise tests. Data points for oral glucose tolerance test (A) represent geometric mean
and corresponding 95% condence interval as error bars from n = 17 participants, and the statistical
analyses are based on natural log transformed data. Data points for submaximal (B) and maximal
(C) exercise tests represent mean and standard deviation as error bars from n = 16 participants. *
Mean [ISFG] was signicantly lower than [CPG] at 15 (p < 0.001) and 30 min (p = 0.041) after glucose
loading. No signicant dierences were found between [ISFG] and [CPG] at each time point during
submaximal and maximal exercise tests.
There were large, positive correlations between paired [ISFG] and [CPG] values dur-
ing the OGTT (r = 0.789, 95% CI 0.69 to 0.86, r2 = 62%, p < 0.001) and the submaximal (r =
0.646, 95% CI 0.44 to 0.79, r2 = 42%, p < 0.001) and maximal (r = 0.622, 95% CI 0.35 to 0.80,
r2 = 39%, p < 0.001) exercise tests. Bland–Altman plots showed lower [ISFG] than [CPG]
(i.e., negative biases) during the OGTT (−0.47 mmol∙L−1, p < 0.001; 95% LoA, −2.40 to 1.54
mmol∙L−1) and submaximal exercise (−0.25 mmol∙L−1, p = 0.046; 95% LoA, −1.92 to 1.42
mmol∙L−1), but not during the maximal exercise test (0.98, p = 0.326; 95% ratio LoA, 0.75 to
1.27).
Figure 4.
Changes in interstitial fluid glucose ([ISFG], open circle) and capillary plasma glucose
([CPG], open square) concentrations during an oral glucose tolerance test (
A
), submaximal (
B
) and
maximal (
C
) exercise tests. Data points for oral glucose tolerance test (
A
) represent geometric mean
and corresponding 95% confidence interval as error bars from n= 17 participants, and the statistical
analyses are based on natural log transformed data. Data points for submaximal (
B
) and maximal
(
C
) exercise tests represent mean and standard deviation as error bars from n= 16 participants.
* Mean [ISFG] was significantly lower than [CPG] at 15 (p< 0.001) and 30 min (p= 0.041) after glucose
loading. No significant differences were found between [ISFG] and [CPG] at each time point during
submaximal and maximal exercise tests.
4. Discussion
This study was the first to assess the performance of the FreeStyle Libre flash glucose
monitoring system in healthy adolescents in response to feeding and exercise. The overall
MARD versus [CPG] reference values was 13.1
±
8.5%, consistent with previous studies
assessing the same glucose monitoring system in young people [
12
,
18
,
19
] and adults [
35
,
36
]
living with T1D. The performance of FGM was previously assessed against capillary blood
glucose in 87 young people aged 4–17 years with T1D under free-living conditions, and
it resulted in an overall MARD of 13.9% [
12
]. Likewise, results from 78 adolescents aged
11–15 years with T1D reported a slightly higher MARD than ours against a capillary blood
glucose of 13.5% during a summer camp, reflecting comparable accuracy during a free-
living setting that included physical activities [
19
]. Moreover, the sensor performance
was examined under controlled condition simulating real-life events (e.g., meals, exercise,
Sensors 2023,23, 4249 10 of 14
hypo-, and hyperglycaemia) against venous plasma glucose concentrations and reported
an overall MARD of 13.2% in adults with T1D [
36
]. It is worth noting that the blood sources
that the FGM is being compared with (as capillary blood) has shown to be more sensitive
to change than venous samples [
37
]. Nevertheless, these results indicate similar sensor
accuracy in healthy young people, despite greater glycaemic variability in people living
with T1D [12,18,19].
An OGTT was used to induce a large glycaemic excursion and to assess the magnitude
of sensor bias from the laboratory-based reference method used in our paediatric research.
The [ISFG] largely correlated with [CPG] during the OGTT (r
2
= 62%, p< 0.001), and the
MARD during the standardised OGTT was 13.4
±
5.0%. However, FGM demonstrated
a mean
bias of
−
0.47 mmol
·
L
−1
across all glucose measurements during OGTT compared
with the [CPG] reference method. Specifically, the differences between [ISFG] and [CPG]
were statistically significant with large and moderate effect sizes, respectively, after 15
(
−
1.16 mmol
·
L
−1
) and 30 min (
−
0.75 mmol
·
L
−1
) of the OGTT. The large mean difference
at 15 min coincides with the greatest residual assessed, with a MARD of 17.5%. The sensor
discrepancy was reported to increase up to a MARD of 17% when glucose increased rapidly
by more than 0.08 mmol
·
L
−1·
min
−1
[
38
]. Similarly, in a recent study in healthy adults
with obesity, MARDs were found to be the highest (~25%) 15 and 30 min after consuming
a standard
breakfast [
21
]. After consuming an OGTT, glucose absorption in the intestine
increased glucose appearance in blood circulation, with the peak value at 30 min. Yet,
glucose concentration in the interstitial fluid compartment did not match this rise, most
likely due to the time required for the glucose to equilibrate in the
two compartments
(known as the physiological lag) [
39
,
40
]. The FGM estimates blood glucose concentration
by measuring [ISFG] using algorithms [
15
]. Although the [ISFG] peak value lagged by
7 min on average, the mean peak [ISFG] was significantly lower than [CPG] by ~7%
(
−
0.54 mmol
·
L
−1
, d = 0.52), indicating that both physiological lag and systematic bias
may have affected the sensor accuracy [
41
]. Nevertheless, total (tAUC) and incremental
(iAUC) glucose area under the curves, which are commonly used postprandial glycaemic
outcomes, were not significantly different between the two glucose measurement methods.
It is worth noting that a lack of statistically significant differences may not necessarily
mean equivalence of measurement, yet the magnitude of the difference was trivial to small.
A similar study in healthy adults without diabetes showed that the differences from pre-
meal baseline were not significantly different between [ISFG] and [CPG] at all time points,
except at 15 and 30 min after breakfast consumption [
20
]. In addition, a study in healthy
children (9.9
±
1.4 years) without diabetes using the professional version of FreeStyle
Libre has shown comparable glucose AUC values after an OGTT when compared with
intravenously obtained plasma glucose values [
9
]. These results indicate that FGM is
an acceptable
device, reflecting postprandial glycaemic responses that have high relevance
to cardiometabolic disease risk [1].
During exercise, glucose concentration in the interstitial fluid may decrease before
plasma glucose due to increased glucose uptake in the exercising muscles; therefore, some
difference in [ISFG] versus [CPG] would be expected. The FGM read slightly lower than
[CPG] (by 0.25 mmol
·
L
−1
on average) during submaximal exercise (three time points),
but not during maximal exercise (two time points). The [ISFG] was similar to [CPG]
across the five glucose sampling time points taken during the exercise tests. The combined
MARD for mid- and post-submaximal exercise was 15.4%. Similarly, MARD values (16.2 to
19.1%) have been seen when comparing FGM and plasma glucose concentrations during
different walking conditions in healthy adults with obesity [
21
]. Due to the difficulty of
obtaining finger-prick blood samples during maximal exercise, blood samples were taken
after participants reached the exhaustion point when the body was under high metabolic
stress. The findings of our study showed a low (i.e., better accuracy) MARD result of
8.8 ±6.4%
compared to the [CPG] reference values after the maximal exercise test. In
contrast, a recent study with healthy adults reported reduced sensor accuracy (median
ARD of 16.2% with high values in the clinically unsafe zone) when FGM was compared
Sensors 2023,23, 4249 11 of 14
with a glucometer during high intensity intermittent (not maximal) exercise performed
2 h after consuming a carbohydrate-rich breakfast [
22
]. That said, studies examining the
accuracy of FreeStyle Libre during different exercise intensities remain rare. Therefore,
further research with a larger number of glucose pairs is needed to confirm the effect of
exercise on sensor accuracy in different populations.
The magnitude of the differences between the two glucose measurement methods was
larger during OGTT (7%) than during submaximal (5%) and maximal (2%) exercise tests.
Two possible reasons for these differences are (1) the rate of glycaemic change after an OGTT
may be higher than that during or after an exercise bout of short duration, and/or (2) the
sampling frequency was higher during OGTT than exercise tests. Regarding Bland–Altman
analyses, FGM underestimated the laboratory-based reference method for measuring [CPG]
during OGTT and submaximal but not maximal exercise tests. Yet, the 95% LoA were
wide under each condition, which is consistent with other studies assessing the same
glucose monitoring system [
36
,
42
] or other continuous glucose monitoring systems [
43
–
45
].
Unlike blood glucose monitoring systems, there are no universally agreed standards for
assessing CGM accuracy [
31
]. Using the ISO 15197:2013 criteria, our results showed that
only 68% (n= 113) of the paired glucose data met the ISO 15197:2013 criteria and were
within the acceptable range. Although this criterion was developed to define the accuracy
and precision requirements necessary for blood glucose monitoring systems (also known as
glucometer devices), King et al. (2018) reported that ~46% of the blood glucose monitoring
systems currently available on the market failed to meet ISO 15197:2013 [
46
]. Nevertheless,
the Consensus Error Grids (CEG) analyses revealed that all FGM readings were found in
zone A (84%, n= 139) or B (16%, n= 26), indicating that results from FGM are clinically
acceptable for making treatment decisions. These results align with other studies [
20
,
21
,
23
].
Although large inter-individual variability in the sensor accuracy was found in the
present study, the individual factors assessed (e.g., age, sex, BMI, %body fat, waist circum-
ference, maturity status and
.
VO2
peak) did not appear to explain this variation. In support,
some studies have shown that sensor accuracy is independent of individual characteristics
such as age, sex, body mass or BMI in adults [
24
] or young people living with T1D [
12
].
However, other studies using the professional FreeStyle Libre found that sensor accuracy
was lower in overweight/obese children without diabetes compared with normal weight
children [
9
]. Our sample did not vary sufficiently in weight status to be able to identify
such a correlation. Therefore, a larger sample size that includes healthy young people
with heterogeneous weight status is required in future studies to address this specifically.
The glucose monitor was assessed in a single day (i.e., day 2 or 3 of sensor wear), that is,
after 24 h of sensor wear, which was deemed to be the least accurate across the 14 wearing
days [
24
,
47
]. Therefore, its accuracy during the remaining days is not known. Yet, previous
studies have shown stable sensor performance across 14 days apart from the insertion
day [24,47].
A strength of the current study is that glucose concentrations were measured during
a number of different conditions (i.e., postprandially, at rest and during various exercise
intensities). However, a limitation of this was that the number of finger-prick blood samples
for any given condition or time period was restricted, as it was not felt appropriate to
subject the adolescent participants in this study to more frequent sampling. This precluded
the determination of the rate of change in glucose concentration and the effect this had
on the sensor performance. Further research should be undertaken to investigate this
important issue.
5. Conclusions
The FreeStyle Libre Flash glucose monitoring system was found to be a feasible alter-
native to finger prick methods when used to examine postprandial glycaemic responses in
healthy adolescents (an under-researched group), with small to trivial differences between
methods for tAUC and iAUC. Use of FGM during exercise was also found to be acceptable,
with only a small difference between methods found and with FGM concentrations slightly
Sensors 2023,23, 4249 12 of 14
lower than those of finger prick methods during submaximal exercise but not maximal
exercise (most studies to date have examined the performance of the devices at rest). Care
may need to be taken when using FGM during periods of rapid change in glucose concen-
trations, as FGM glucose concentrations were found to be lower after 15 and 30 min of the
OGTT than finger prick methods.
Author Contributions:
Conceptualisation, S.A., L.A.B. and K.T.; methodology, S.A., L.A.B. and
K.T.; formal analysis, S.A. and J.K.Z.-F.; investigation, S.A.; resources, K.T.; data curation, S.A.;
writing—original
draft preparation, S.A.; writing—review and editing, K.T., J.K.Z.-F. and L.A.B.;
supervision, K.T. and L.A.B.; project administration, K.T.; funding acquisition, K.T. All authors have
read and agreed to the published version of the manuscript.
Funding:
This research was funded by King Abdulaziz University, Jeddah, Saudi Arabia, through
a PhD scholarship to Sahar Afeef.
Institutional Review Board Statement:
This study was conducted in accordance with the guidelines
of the Declaration of Helsinki and approved by the ethical standards of Loughborough University
Ethics Committee (HPSC reference number: R19-P147). The ethical approval was obtained in
October 2019.
Informed Consent Statement:
Each of the participant’s parents/guardians provided written in-
formed consent, with participants giving their written assent to be involved in the study.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to ethical reasons of patient data.
Acknowledgments:
The authors thank the participants for their commitment, and Jessica Flint and
Jelena Sekulic for their assistance with data collection.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
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